CN115882480A - Energy storage system optimization control method and system considering power grid frequency and voltage support - Google Patents
Energy storage system optimization control method and system considering power grid frequency and voltage support Download PDFInfo
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Abstract
The invention discloses an energy storage system optimization control method and system considering power grid frequency and voltage support, which comprises the following steps: firstly, establishing a multi-objective function and a constraint condition under the participation of an energy storage system according to the technical requirements of the frequency and the voltage of a power grid and a regulation and control mechanism; then, inputting the predicted power change of the power system and the state of the energy storage system in a future period of time in the model; and finally, forming the optimal active power output and the optimal reactive power output of the energy storage system by utilizing an alternative iterative optimization idea, and completing the effective support of the frequency and the voltage of the power grid. By modeling and optimizing the energy storage system for actively supporting the frequency and the voltage of the power grid, the comprehensive requirements of the frequency and the voltage of the power grid are effectively considered under the condition of considering the actual state of the energy storage system, the comprehensive service capability of the energy storage system is furthest exerted, and the auxiliary service level of the energy storage system on the electric power is favorably improved.
Description
Technical Field
The invention belongs to the technical field of energy storage systems, and particularly relates to an energy storage system optimization control method and system considering grid frequency and voltage support.
Background
With the increasing deepening of high-proportion new energy and high-proportion power electronic equipment in a novel power system, the operation safety and reliability of a power grid face severe challenges. The frequency deviation under the unbalanced active power of the power grid caused by the randomness of the high-proportion new energy and the voltage fluctuation problem under the unbalanced reactive power of the power grid caused by the high-proportion power electronic equipment are more serious. As an energy storage system with flexible and controllable resources, the system has the functions of improving the quality of electric energy, actively supporting tide, dynamically performing reactive power compensation and the like, and can reduce the influence through active effective control, thereby greatly improving the operation safety of a power grid. How to mine the application potential of the energy storage system and reasonably maximize the service capacity of the energy storage system has become a hot topic of the application research of the energy storage system.
At present, experts and scholars at home and abroad develop a great deal of research on providing specific single power auxiliary service for an energy storage system at a power supply side, a power grid side and a user side, the control and planning technology of the energy storage system around the auxiliary service requirements of new energy consumption, power peak regulation, voltage regulation, frequency regulation and the like is mature day by day, and the control problem of a multi-target energy storage system facing a plurality of power service subjects and various power service requirements is required to be improved. Meanwhile, with the construction of a novel power system, new energy and an energy storage system also begin to develop from a traditional network following type to a network construction type. This means that energy storage system control needs to move further towards power service initiatives and power service compatibility.
Disclosure of Invention
The invention provides an energy storage system optimization control method and system considering power grid frequency and voltage support, which are used for solving the technical problem that high-proportion new energy and distributed power supply access influence power grid frequency and voltage fluctuation.
In a first aspect, the present invention provides an energy storage system optimization control method considering grid frequency and voltage support, including: according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed; acquiring a predicted power value of each node of the power system in a future T period; initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T time period; inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value; judging whether the current iteration times reach the maximum iteration times or not; if not, judging whether the active power output sequence error between two adjacent iterations is greater than a first set threshold value or not and whether the reactive power output sequence error is greater than a second set threshold value or not; and if the active power output sequence error between two adjacent iterations is not greater than a first set threshold and the reactive power output sequence error is not greater than a second set threshold, acquiring the active power and the reactive power optimally controlled by the energy storage system at the current moment, and entering the optimal control at the next moment.
In a second aspect, the present invention provides an energy storage system optimization control system considering grid frequency and voltage support, comprising: the building module is configured to build a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system; the acquisition module is configured to acquire a predicted power value on each node of the power system in a future T period; the initialization module is configured to initialize a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period; the calculation module is configured to input the reactive power output sequence into the first model, calculate and obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, input the optimal output active power sequence into the second model, and calculate and obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value; the first judgment module is configured to judge whether the current iteration times reach the maximum iteration times; the second judgment module is configured to judge whether the active power output sequence error between two adjacent iterations is greater than a first set threshold value and whether the reactive power output sequence error is greater than a second set threshold value if the active power output sequence error is not greater than the second set threshold value; and the control module is configured to obtain the active power and the reactive power of the energy storage system at the current moment and enter the next moment for optimal control if the active power output sequence error between two adjacent iterations is not greater than a first set threshold and the reactive power output sequence error is not greater than a second set threshold.
In a third aspect, an electronic device is provided, comprising: the energy storage system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the steps of the energy storage system optimization control method considering grid frequency and voltage support of any embodiment of the invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which program instructions, when executed by a processor, cause the processor to perform the steps of the energy storage system optimization control method of any of the embodiments of the present invention taking into account grid frequency and voltage support.
The energy storage system optimization control method and system considering power grid frequency and voltage support have the following specific beneficial effects:
through a simultaneous energy storage system frequency modulation and voltage regulation mathematical model, a unified optimization model facing to power grid frequency modulation and voltage is formed to provide an optimization basis for energy storage system control, the capacity that an energy storage system can output active power and reactive power at the same time is reflected, and an optimal control process considering future power supply and utilization power change and energy storage system state constraint is formed by combining a model prediction control idea, so that the power auxiliary service capacity of the energy storage system can be exerted to the maximum extent; and the coupling effect of active output and reactive output in the frequency modulation process of the energy storage system and the voltage regulation process is fully considered, and the optimization process of alternating iteration of the active output power and the reactive output power is utilized, so that the optimal control of the energy storage system considering the requirements of power frequency modulation and voltage regulation is effectively formed, the complexity of model optimization is reduced, the optimization control efficiency is improved, and the method has important significance for the energy storage system to actively support the grid frequency and voltage service.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a diagram of a 33-node power system architecture according to an embodiment of the present invention;
fig. 2 is a flowchart of an energy storage system optimization control method considering grid frequency and voltage support according to an embodiment of the present invention;
fig. 3 is a block diagram of an energy storage system optimization control system with consideration of grid frequency and voltage support according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Taking the 33-node power network structure shown in fig. 1 as an example, it is indicated in the figure that a thermal power plant is configured at a node 1, 2 photovoltaic power plants are accessed at nodes 9 and 16, 2 wind power plants are accessed at nodes 22 and 33, 1 energy storage system is accessed at a node 6, a load user is at each node (such as nodes 4 and 11), and the photovoltaic power plants and the wind power plants only output active power and do not perform virtual inertia control. The iterative optimization solving process for realizing the energy storage system with consideration of both the grid frequency and the voltage support by applying the technology of the invention is shown in figure 2. The energy storage system optimization control method considering the grid frequency and voltage support specifically comprises the following steps of:
step S101, according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed.
In this embodiment, the first model includes an active power constraint condition of a power system frequency modulation control process and a frequency modulation control objective function under the active power constraint condition, and the second model includes an operation constraint condition of a power system voltage regulation control process and a voltage regulation control objective function under the operation constraint condition.
It should be noted that the active power constraint conditions include:
the system comprises a differential equation of the frequency change rate and the active power change quantity of the system, a system equivalent inertia constraint, a thermal power unit active power change quantity constraint, a photovoltaic power station active power change quantity constraint, a wind power plant active power change quantity constraint, an energy storage system active power change quantity constraint, an energy storage energy state constraint, a frequency maximum deviation, a frequency deviation change rate constraint, a power supply rated power constraint, a thermal power unit ramp rate constraint and an energy storage energy state limit constraint, wherein the specific calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
for the rated frequency of the mains>
For equivalent inertia of the system>
For system capacity, <' > based on>
For a change of the system frequency at time t, <' >>
For a system equivalent damping coefficient>
For the active power variation of the thermal power generating unit at the moment t of the ith power system node, based on the comparison result>
For the active power variation of the photovoltaic power station at the moment t of the ith power system node, combining the variable value and the preset value>
The active power change quantity of the wind power plant at the moment t for the ith power system node is changed in a manner of combining the change quantity and the active power change quantity>
The active power variation of the energy storage system at the moment t for the ith power system node is changed, and>
the active power variation of the load user at the moment t for the ith power system node;
in the formula (I), the compound is shown in the specification,
is an inertia factor->
For inertia coefficient of live motor group on ith power system node, based on the inertia coefficient of live motor group on the ith power system node>
For the rated power of the live generator set on the ith power system node, based on the rated power value of the live generator set on the ith power system node>
For the inertia coefficient of the photovoltaic power station on the ith power system node>
For the rated power of the photovoltaic power station on the ith power system node>
Is the inertia coefficient of the wind farm on the ith power system node>
For the rated power of the wind farm at the ith power system node>
For the inertia coefficient of the energy storage system on the ith power system node>
The rated power of the energy storage system on the ith power system node;
in the formula (I), the compound is shown in the specification,
for system frequency at time t>
For the system frequency at the initial moment>
For the active power of the thermal power unit at the moment t of the ith power system node, combining>
For the active power of the thermal power generating unit at the initial moment of the ith power system node, the judgment is made>
For the active power of the photovoltaic power station at the moment t of the ith power system node>
For the active power of the photovoltaic power station at the initial moment of the ith power system node>
Active power of wind power plant at time t for ith power system node>
The active power of the wind farm at the initial moment for the ith power system node, device for combining or screening>
For the active power of the energy storage system at the moment t of the ith power system node, based on the comparison result of the comparison result>
The active power of the energy storage system at the initial moment is the ith power system node;
in the formula (I), the compound is shown in the specification,
is the ithEnergy storage system on each power system node>
The energy state at the moment in time,
for the energy state at the moment t of the energy storage system on the ith power system node, in a manner of combining the energy state and the energy state>
Charging efficiency for an energy storage system>
For a time interval>
For the capacity of the energy storage system on the ith power system node, is greater than or equal to>
Discharging efficiency for the energy storage system;
in the formula (I), the compound is shown in the specification,
allowable threshold for frequency deviations>
Allowing a threshold for a frequency change rate>
For the maximum output active power of the photovoltaic power station on the ith power system node at the time t, the maximum output active power is combined>
For the maximum output active power of the wind farm at the point of time t on the ith power system node, in combination with>
For the energy storage system on the ith power system node at tReactive power at a moment;
in the formula (I), the compound is shown in the specification,
is at for the ith power system node>
Active power of constantly-fired power unit>
The climbing threshold value of the live working motor group is the ith power system node;
in the formula (I), the compound is shown in the specification,
is the lower limit value of the energy state of the energy storage system on the ith power system node, is greater than or equal to>
The energy state of the energy storage system on the ith power system node is the upper limit value;
the frequency modulation control objective function under the active power constraint condition is specifically as follows: the active power of the thermal power generating unit is minimized, the active power output of new energy is maximized, and the expression is as follows:
in the formula (I), the compound is shown in the specification,
for number of power system nodes, in combination with a timer>
For predicting the step size, <' >>
Are time intervals.
Wherein the operating constraints include:
the method comprises the following steps of power system active power constraint, power system reactive power balance constraint, node voltage and power constraint, thermal power unit rated power constraint, maximum active regulating quantity constraint, maximum reactive regulating quantity constraint, climbing speed constraint, photovoltaic power station active power output constraint, wind power plant active power output constraint, energy storage energy balance constraint, energy state limit value and rated power constraint;
in the formula (I), the compound is shown in the specification,
for the active power of the thermal power generating unit at the moment t of the ith power system node, the judgment is made>
Active power of an energy storage system at the moment t for the ith power system node>
For the active power of the photovoltaic power station at the moment t of the ith power system node, combining>
Active power of the wind farm at time t for the ith power system node>
Active power of a load user at the moment t for the ith power system node>
The active power output from the ith power system node to the jth power system node at the time t is combined>
For the ith power system node at the moment tA current output to the jth power system node, <' > or>
Is the resistance between node j and node i->
The reactive power of the thermal power generating unit at the moment t of the ith power system node,
for the reactive power of the energy storage system at the moment t of the ith power system node, the value is greater than or equal to>
For the reactive power of the load subscriber at the time t of the ith power system node, in conjunction with the control of the load subscriber at the time t>
The reactive power which is output from the ith power system node to the jth power system node at the moment t is changed into the value>
Is the impedance between node j and node i;
in the formula (I), the compound is shown in the specification,
for the voltage at node i at time t, < >>
Is the voltage at node j at time t; />
In the formula (I), the compound is shown in the specification,
for the energy storage system on the ith power system node>
The energy state at the moment in time,
for the energy state at the moment t of the energy storage system on the ith power system node, is greater than or equal to>
In order to provide the charging efficiency for the energy storage system,
active power of an energy storage system at the moment t for the ith power system node>
For a time interval>
For the capacity of the energy storage system on the ith power system node, is greater than or equal to>
Discharging efficiency for the energy storage system;
in the formula (I), the compound is shown in the specification,
for the active power of the thermal power generating unit at the moment t of the ith power system node, the judgment is made>
For the reactive power of the thermal power generating unit at the moment t of the ith power system node, the value is greater than or equal to>
The rated power of the live generating set is obtained at the ith power system node,
the minimum output active power of the ignition motor group for the ith power system node is greater than or equal to>
The maximum output active power of the live motor group on fire for the ith power system node is greater than or equal to>
Is at for the ith power system node>
Active power of fire power unit at any moment>
A climbing threshold value for an on-fire motor group of the ith power system node is preset>
The minimum output reactive power of the live motor group on the ith power system node is greater than or equal to>
The maximum output reactive power of the live working motor group is greater than or equal to the maximum output reactive power of the ith power system node>
For the active power of the photovoltaic power station at the moment t of the ith power system node, combining>
For the maximum output active power of the photovoltaic power station on the ith power system node at the time t, the maximum output active power is combined>
The active power of the wind farm at time t for the ith power system node,
for the maximum output active power of the wind farm at the point of time t on the ith power system node, in combination with>
Is the lower limit value of the voltage of the node i, and is greater than or equal to>
For node i voltage upper limit value,/>>
For the branch to pass the maximum current->
For the lower limit value of the energy state of the energy storage system on the ith power system node, in a manner of changing the voltage level in the storage system>
Is the upper limit value of the energy state of the energy storage system on the ith power system node,
the rated power of the energy storage system on the ith power system node;
the voltage regulation control objective function under the operation constraint condition is specifically as follows: in the prediction period, the sum of squares of the active power and the reactive power output by the energy storage system is minimized, namely the output of the energy storage system is minimum, and the expression is as follows:
in the formula (I), the compound is shown in the specification,
for number of power system nodes, in combination with a timer>
For a prediction step, <' >>
For a time interval>
For the energy storage system on the ith power system node at (t) 0 Active power output at + k Δ t) time, in conjunction with the activation signal>
For the energy storage system on the ith power system node at (t) 0 Reactive power output at time + k Δ t)，t 0 Indicating the current time of day.
Step S102, obtaining the predicted power value of each node of the power system in the future T period.
In the embodiment, an active and reactive short-term prediction model of a load user and an active power short-term prediction model of a photovoltaic power station and a wind power plant are established by utilizing regression models such as a neural network and a support vector machine according to a large amount of historical data of the load user and a large amount of historical data of the photovoltaic power station and the wind power plant;
based on data of load user nodes at the current time T, data of photovoltaic power stations and data of wind power plant nodes, active power of load users in the future T period is obtained according to a short-term prediction model
Reactive power
Maximum active power of photovoltaic power station>
And the maximum active power of the wind farm->
。
And step S103, initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T time period.
And S104, inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value.
In this embodiment, substituting a reactive power output sequence of the energy storage system in a future T period into the first model to update the active power constraint in the first model, where an expression of the updated active power constraint is:
in the formula (I), the compound is shown in the specification,
for the active power of the energy storage system at the moment t of the ith power system node, based on the comparison result of the comparison result>
The reactive power of the energy storage system at the moment t is greater or less than the reactive power of the ith power system node in the nth iteration>
The rated power of the energy storage system on the ith power system node;
optimizing the first model based on a preset classical optimization method to obtain an optimal output active power sequence of the energy storage system for frequency modulation, namely
. The classical optimization method comprises Lagrange optimization, gradient descent, a simplex method and the like.
Further, substituting the optimal output active power sequence of the energy storage system in the future T period into the second model to update the operation constraint condition in the second model and the voltage regulation control objective function under the operation constraint condition, wherein the expression of the updated operation constraint condition is as follows:
in the formula (I), the compound is shown in the specification,
for the active power of the thermal power unit at the moment t of the ith power system node, combining>
The energy storage system of the ith power system node at the moment t during the r iterationActive power of (D), in combination with>
For the active power of the photovoltaic power station at the moment t of the ith power system node>
Active power of the wind farm at time t for the ith power system node>
For the active power of the load user of the ith power system node at the time t, based on the real power of the load user>
The active power output from the ith power system node to the jth power system node at the moment t is changed>
Is the current output by the ith power system node to the jth power system node at the moment t, is->
Is the resistance between the node j and the node i;
in the formula (I), the compound is shown in the specification,
for the energy storage system on the ith power system node>
Temporal energy state>
For the energy state at the moment t of the energy storage system on the ith power system node, in a manner of combining the energy state and the energy state>
Charging efficiency for an energy storage system>
In the form of a time interval,
for the capacity of the energy storage system on the ith power system node, is greater than or equal to>
Discharging efficiency for the energy storage system;
in the formula (I), the compound is shown in the specification,
the reactive power of the energy storage system at the time t of the ith power system node in the r iteration,
the rated power of the energy storage system on the ith power system node;
the expression of the updated voltage regulation control objective function is as follows:
in the formula (I), the compound is shown in the specification,
for the energy storage system on the ith power system node att 0 +kΔt) Active power output at any moment>
For the energy storage system on the ith power system node att 0 +kΔt) The reactive power output at any moment in time,t 0 represents the current time instant, <' > based on>
For number of power system nodes, in combination with a timer>
For a prediction step, <' >>
Are time intervals.
Step S105, judging whether the current iteration times reach the maximum iteration times.
In this embodiment, it is determined that the iteration number R is greater than or equal to the maximum iteration number R, and if yes, the current iteration number R is obtained
Energy storage system optimization control based on consideration of active support of power grid frequency and voltage at any moment>
，/>
Is the ith power system node being ^ at ^ 1 iterations>
Active power of the moment energy storage system->
Is that the ith power system node at the R < th > iteration is >>
And the reactive power of the energy storage system at the moment enters the optimized control of the energy storage system at the next moment.
Step S106, if not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value and whether the reactive power output sequence error is larger than a second set threshold value.
Step S107, if the error of the active power output sequence between two adjacent iterations is not greater than a first set threshold and the error of the reactive power output sequence is not greater than a second set threshold, obtaining the active power and the reactive power of the energy storage system at the current moment, and entering the next moment.
In this embodiment, the active power output is determinedThe sequence error is not more than the first set threshold
And the reactive power output sequence error is not greater than a second set threshold value->
If the following conditions are met:
In summary, the method mainly comprises an energy storage frequency modulation and voltage regulation control optimization method under the condition that the energy storage system participates in power frequency modulation and voltage regulation service modeling and predicts power information input. According to the technical requirements of the frequency and the voltage of the power grid and a regulation and control mechanism, establishing a multi-objective function and a constraint condition under the participation of an energy storage system; then, inputting the predicted power change of the power system and the state of the energy storage system in a future period of time in the model; and finally, forming the optimal active power output and the optimal reactive power output of the energy storage system by utilizing an alternative iterative optimization idea, and completing the effective support of the frequency and the voltage of the power grid. By modeling and optimizing the energy storage system for actively supporting the frequency and the voltage of the power grid, the comprehensive requirements of the frequency and the voltage of the power grid are effectively considered under the condition of considering the actual state of the energy storage system, the comprehensive service capability of the energy storage system is furthest exerted, and the auxiliary service level of the energy storage system on the electric power is favorably improved.
Referring to fig. 3, a block diagram of an energy storage system optimization control system with grid frequency and voltage support taken into account is shown.
As shown in FIG. 3, the optimization control system 200 includes a construction module 210, an obtaining module 220, an initialization module 230, a calculation module 240, a first determination module 250, a second determination module 260, and a control module 270.
The building module 210 is configured to build a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system; an obtaining module 220 configured to obtain a predicted power value at each node of the power system in a future T period; an initialization module 230 configured to initialize a reactive power output sequence and a maximum number of iterations of the energy storage system in a future T period; the calculating module 240 is configured to input the reactive power output sequence into the first model, calculate an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, input the optimal output active power sequence into the second model, and calculate an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value; a first determining module 250 configured to determine whether the current iteration count reaches a maximum iteration count; a second determining module 260, configured to determine whether an active power output sequence error between two adjacent iterations is greater than a first set threshold and whether a reactive power output sequence error is greater than a second set threshold if the active power output sequence error does not reach the second set threshold; and the control module 270 is configured to, if the active power output sequence error between two adjacent iterations is not greater than the first set threshold and the reactive power output sequence error is not greater than the second set threshold, obtain the active power and the reactive power for the energy storage system optimization control at the current moment, and enter the optimization control at the next moment.
It should be understood that the modules recited in fig. 3 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 3, and are not described again here.
In other embodiments, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to execute the method for energy storage system optimization control in any of the above method embodiments, which takes grid frequency and voltage support into account;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed;
acquiring a predicted power value of each node of the power system in a future T period;
initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period;
inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
judging whether the current iteration times reach the maximum iteration times or not;
if not, judging whether the active power output sequence error between two adjacent iterations is greater than a first set threshold value or not and whether the reactive power output sequence error is greater than a second set threshold value or not;
and if the active power output sequence error between two adjacent iterations is not greater than a first set threshold and the reactive power output sequence error is not greater than a second set threshold, acquiring the active power and the reactive power of the energy storage system at the current moment, and entering the next moment for optimal control.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from the use of an energy storage system optimization control system that accounts for grid frequency and voltage support, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located from the processor, which may be connected over a network to an energy storage system optimization control system that accounts for grid frequency and voltage support. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 4. The memory 320 is the computer-readable storage medium described above. The processor 310 executes the non-volatile software programs, instructions and modules stored in the memory 320, so as to execute various functional applications of the server and data processing, that is, to implement the energy storage system optimization control method considering grid frequency and voltage support according to the above method embodiment. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the energy storage system optimization control system taking into account grid frequency and voltage support. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to an energy storage system optimization control system that considers grid frequency and voltage support, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed;
acquiring a predicted power value of each node of the power system in a future T period;
initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T time period;
inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
judging whether the current iteration times reach the maximum iteration times or not;
if not, judging whether the active power output sequence error between two adjacent iterations is greater than a first set threshold value or not and whether the reactive power output sequence error is greater than a second set threshold value or not;
and if the active power output sequence error between two adjacent iterations is not greater than a first set threshold and the reactive power output sequence error is not greater than a second set threshold, acquiring the active power and the reactive power optimally controlled by the energy storage system at the current moment, and entering the optimal control at the next moment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of various embodiments or some parts of embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An energy storage system optimization control method considering grid frequency and voltage support is characterized by comprising the following steps:
according to the obtained power network structure information, power supply information, load user information and physical information of the energy storage system, a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation are constructed;
acquiring a predicted power value of each node of the power system in a future T period;
initializing a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period;
inputting the reactive power output sequence into the first model, calculating to obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, inputting the optimal output active power sequence into the second model, and calculating to obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
judging whether the current iteration times reach the maximum iteration times or not;
if not, judging whether the active power output sequence error between two adjacent iterations is larger than a first set threshold value or not and whether the reactive power output sequence error is larger than a second set threshold value or not;
and if the active power output sequence error between two adjacent iterations is not greater than a first set threshold and the reactive power output sequence error is not greater than a second set threshold, acquiring the active power and the reactive power optimally controlled by the energy storage system at the current moment, and entering the optimal control at the next moment.
2. The energy storage system optimization control method considering grid frequency and voltage support according to claim 1, wherein the first model includes an active power constraint condition of a power system frequency modulation control process and a frequency modulation control objective function under the active power constraint condition, and the second model includes an operation constraint condition of a power system voltage regulation control process and a voltage regulation control objective function under the operation constraint condition.
3. The method of claim 2, wherein the active power constraints include:
the system comprises a differential equation of the frequency change rate and the active power change quantity of the system, a system equivalent inertia constraint, a thermal power unit active power change quantity constraint, a photovoltaic power station active power change quantity constraint, a wind power plant active power change quantity constraint, an energy storage system active power change quantity constraint, an energy storage energy state constraint, a frequency maximum deviation, a frequency deviation change rate constraint, a power supply rated power constraint, a thermal power unit ramp rate constraint and an energy storage energy state limit constraint, wherein the specific calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
for the nominal frequency of the mains>
For the equivalent inertia of the system, is>
For system capacity, <' > based on>
For a change of the system frequency at time t, <' >>
For a system equivalent damping coefficient>
For the active power variation of the thermal power generating unit at the moment t of the ith power system node, based on the comparison result>
For the active power variation of the photovoltaic power station at the ith power system node at the moment t,
for the active power variation of the ith power system node at the moment t, based on the value of the active power of the wind power plant, the value is greater than or equal to>
The active power variation of the energy storage system at the moment t for the ith power system node is changed, and>
the active power variation of the load user at the moment t is the ith power system node; />
In the formula (I), the compound is shown in the specification,
is an inertia factor->
For inertia coefficient of live motor group on ith power system node, based on the inertia coefficient of live motor group on the ith power system node>
For the rated power of the live generator set on the ith power system node, based on the rated power value of the live generator set on the ith power system node>
For the inertia coefficient of the photovoltaic power station on the ith power system node>
For the rated power of the photovoltaic power station on the ith power system node>
For the inertia coefficient of the wind farm in the ith power system node>
For the rated power of the wind farm at the ith power system node>
Is an inertia coefficient of an energy storage system on the ith power system node>
The rated power of the energy storage system on the ith power system node;
in the formula (I), the compound is shown in the specification,
for the system frequency at time t, < >>
For the system frequency at the initial moment>
For the active power of the thermal power generating unit at the moment t of the ith power system node, the judgment is made>
For the active power of the thermal power generating unit at the initial moment of the ith power system node, the judgment is made>
For the active power of the photovoltaic power station at the moment t of the ith power system node, combining>
For the active power of the photovoltaic power station at the initial moment of the ith power system node, combining>
Active power of the wind farm at time t for the ith power system node>
Active power of the wind farm at the initial moment is ^ h for the ith power system node>
For the active power of the energy storage system at the moment t of the ith power system node, based on the comparison result of the comparison result>
The active power of the energy storage system at the initial moment is the ith power system node;
in the formula (I), the compound is shown in the specification,
for the energy storage system on the ith power system node>
The energy state at that moment is taken into effect>
For the energy state at the moment t of the energy storage system on the ith power system node, in a manner of combining the energy state and the energy state>
Charging efficiency for the energy storage system>
In the form of a time interval,
for the capacity of the energy storage system on the ith power system node, is greater than or equal to>
Discharging efficiency for the energy storage system; />
In the formula (I), the compound is shown in the specification,
allowing a threshold value for a frequency deviation>
Allowing a threshold value for the rate of change of frequency>
For the maximum output active power of the photovoltaic power station on the ith power system node at the moment t, is greater or less than>
For the maximum output active power of the wind farm at the point of time t on the ith power system node, in combination with>
The reactive power of an energy storage system on the ith power system node at the time t is measured;
in the formula (I), the compound is shown in the specification,
is at for the ith power system node>
Active power of fire power unit at any moment>
The climbing threshold value of the live working motor group is the ith power system node;
in the formula (I), the compound is shown in the specification,
for the lower limit value of the energy state of the energy storage system on the ith power system node, in a manner of changing the voltage level in the storage system>
The energy state of the energy storage system on the ith power system node is the upper limit value;
the frequency modulation control objective function under the active power constraint condition is specifically as follows: the active power of the thermal power generating unit is minimized, the active power output of new energy is maximized, and the expression is as follows:
in the formula (I), the compound is shown in the specification,
is the number of nodes in the power system, and>
for a prediction step, <' >>
Are time intervals.
4. The method of claim 2, wherein the operating constraints comprise:
the method comprises the following steps of power system active power constraint, power system reactive power balance constraint, node voltage and power constraint, thermal power unit rated power constraint, maximum active regulating quantity constraint, maximum reactive regulating quantity constraint, climbing speed constraint, photovoltaic power station active power output constraint, wind power plant active power output constraint, energy storage energy balance constraint, energy state limit value and rated power constraint;
in the formula (I), the compound is shown in the specification,
for the active power of the thermal power generating unit at the moment t of the ith power system node, the judgment is made>
For the active power of the energy storage system at the moment t of the ith power system node, based on the comparison result of the comparison result>
For the active power of the photovoltaic power station at the moment t of the ith power system node, combining>
Active power of the wind farm at time t for the ith power system node>
Active power of a load user at the moment t for the ith power system node>
The active power output from the ith power system node to the jth power system node at the moment t is changed>
The current outputted from the ith power system node to the jth power system node at the time t is combined>
Is the resistance between node j and node i->
For the reactive power of the thermal power generating unit at the moment t of the ith power system node, the value is greater than or equal to>
For the reactive power of the energy storage system at the moment t of the ith power system node, the value is greater than or equal to>
For the reactive power of the load subscriber at the moment t of the ith power system node, ->
The reactive power which is output from the ith power system node to the jth power system node at the time t is combined>
Is the impedance between node j and node i;
in the formula (I), the compound is shown in the specification,
for the voltage at node i at time t, < >>
Is the voltage at node j at time t;
in the formula (I), the compound is shown in the specification,
for the energy storage system on the ith power system node>
The energy state at that moment is taken into effect>
For the energy state at the moment t of the energy storage system on the ith power system node, in a manner of combining the energy state and the energy state>
Charging efficiency for the energy storage system>
Active power of an energy storage system at the moment t for the ith power system node>
For a time interval>
For the capacity of the energy storage system on the ith power system node, is greater than or equal to>
Discharging efficiency for the energy storage system;
in the formula (I), the compound is shown in the specification,
for the active power of the thermal power generating unit at the moment t of the ith power system node, the judgment is made>
For the reactive power of the thermal power generating unit at the moment t of the ith power system node, the value is greater than or equal to>
The rated power of the live working motor group on the ith power system node is greater than or equal to>
The minimum output active power of the live generator set on the fire of the ith power system node is combined with the preset power value>
The maximum output active power of the live motor group on fire for the ith power system node is greater than or equal to>
Is on/for ith power system node>
Active power of fire power unit at any moment>
A climbing threshold value for an on-fire motor group of the ith power system node is preset>
The minimum output reactive power of the live motor group on the ith power system node is greater than or equal to>
For the maximum output reactive power of the live generator set on the ith power system node, on or off>
For the active power of the photovoltaic power station at the moment t of the ith power system node>
For the maximum output active power of the photovoltaic power station on the ith power system node at the moment t, is greater or less than>
Active power of the wind farm at time t for the ith power system node>
For the maximum output active power of the wind farm at the moment t on the ith power system node, is greater than or equal to>
For a lower limit value of the voltage of node i, in combination with a voltage regulation>
For the upper limit value of the voltage at node i>
For the branch to pass the maximum current->
For the lower limit value of the energy state of the energy storage system on the ith power system node, in a manner of changing the voltage level in the storage system>
Is the upper limit value of the energy storage system energy state on the ith power system node, is greater than or equal to>
The rated power of the energy storage system on the ith power system node;
the voltage regulation control objective function under the operation constraint condition is specifically as follows: in the prediction period, the square sum of the active power and the reactive power output by the energy storage system is minimized, namely the output of the energy storage system is minimum, and the expression is as follows:
in the formula (I), the compound is shown in the specification,
is the number of nodes in the power system, and>
for predicting the step size, <' >>
For a time interval>
For the energy storage system on the ith power system node is at (t) 0 Active power output at time + k Δ t)>
For the energy storage system on the ith power system node at (t) 0 + k Δ t) time, t 0 Indicating the current time of day.
5. The energy storage system optimization control method considering grid frequency and voltage support according to claim 1, wherein each node of the power system is a load user node, a photovoltaic power station node and a wind farm node; the obtaining of the predicted power value at each node of the power system in the future T period includes:
acquiring historical data of load user nodes, historical data of photovoltaic power station nodes and historical data of wind power station nodes, and establishing a short-term prediction model based on a preset regression model, wherein the regression model is a neural network regression model or a support vector machine model, and the short-term prediction model comprises an active and reactive short-term prediction model of the load user nodes, an active power short-term prediction model of the photovoltaic power station nodes and an active power short-term prediction model of the wind power station nodes;
based on the data of the load user node at the current time T, the data of the photovoltaic power station node and the data of the wind power plant node, the active power of the load user node in the future T period is obtained according to the short-term prediction model
And the reactive power->
Maximum active power of a photovoltaic power station node>
And maximum active power ^ of the wind farm node>
。
6. The method as claimed in claim 1, wherein the inputting the reactive power output sequence into the first model and calculating an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value comprises:
substituting a reactive power output sequence of the energy storage system in a future T period into the first model to update the active power constraint in the first model, wherein the updated active power constraint has an expression as follows:
in the formula (I), the compound is shown in the specification,
active power of an energy storage system at the moment t for the ith power system node>
The reactive power of the energy storage system at the moment t is greater or less than the reactive power of the ith power system node in the nth iteration>
The rated power of the energy storage system on the ith power system node; />
Optimizing the first model based on a preset classical optimization method to obtain an optimal output active power sequence of the energy storage system for frequency modulation, namely
。
7. The energy storage system optimization control method considering grid frequency and voltage support according to claim 1, wherein the inputting the optimal output active power sequence into the second model and calculating an optimal output reactive power sequence for voltage regulation of the energy storage system based on the predicted power value comprises:
substituting the optimal output active power sequence of the energy storage system in the future T period into the second model to update the operation constraint condition in the second model and the voltage regulation control objective function under the operation constraint condition, wherein the expression of the updated operation constraint condition is as follows:
in the formula (I), the compound is shown in the specification,
thermal power engine for ith power system node at time tActive power of the group->
The active power of the energy storage system at the moment t of the ith power system node in the r iteration is combined>
For the active power of the photovoltaic power station at the moment t of the ith power system node>
Active power of the wind farm at time t for the ith power system node>
For the active power of the load user of the ith power system node at the time t, based on the real power of the load user>
The active power output from the ith power system node to the jth power system node at the moment t is changed>
The current outputted from the ith power system node to the jth power system node at the time t is combined>
Is the resistance between node j and node i;
in the formula (I), the compound is shown in the specification,
for an energy storage system on the ith power system node>
The energy state at that moment is taken into effect>
For the energy state at the moment t of the energy storage system on the ith power system node, is greater than or equal to>
Charging efficiency for the energy storage system>
For a time interval>
For the capacity of the energy storage system on the ith power system node, is greater than or equal to>
Discharging efficiency for the energy storage system;
in the formula (I), the compound is shown in the specification,
the reactive power of the energy storage system at the moment t is greater or less than the reactive power of the ith power system node in the nth iteration>
The rated power of the energy storage system on the ith power system node;
the expression of the updated voltage regulation control objective function is as follows:
in the formula (I), the compound is shown in the specification,
for the energy storage system on the ith power system node at (t) 0 Active power output at time + k Δ t)>
For the energy storage system on the ith power system node at (t) 0 + k Δ t) time, t 0 Represents the current time instant, <' > based on>
Is the number of nodes in the power system, and>
for a prediction step, <' >>
Are time intervals. />
8. An energy storage system optimization control system that accounts for grid frequency and voltage support, comprising:
the building module is configured to build a first model of the energy storage system participating in power frequency modulation and a second model of the energy storage system participating in power grid voltage regulation according to the acquired power network structure information, power supply information, load user information and physical information of the energy storage system;
the acquisition module is configured to acquire a predicted power value on each node of the power system in a future T period;
the initialization module is configured to initialize a reactive power output sequence and the maximum iteration number of the energy storage system in a future T period;
the calculation module is configured to input the reactive power output sequence into the first model, calculate and obtain an optimal output active power sequence of the energy storage system for frequency modulation based on the predicted power value, input the optimal output active power sequence into the second model, and calculate and obtain an optimal output reactive power sequence of the energy storage system for voltage regulation based on the predicted power value;
the first judgment module is configured to judge whether the current iteration times reach the maximum iteration times;
the second judgment module is configured to judge whether the active power output sequence error between two adjacent iterations is greater than a first set threshold value or not and whether the reactive power output sequence error is greater than a second set threshold value or not if the active power output sequence error is not greater than the first set threshold value;
and the control module is configured to obtain the active power and the reactive power of the energy storage system at the current moment and enter the next moment for optimal control if the active power output sequence error between two adjacent iterations is not greater than a first set threshold and the reactive power output sequence error is not greater than a second set threshold.
9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180034285A1 (en) * | 2016-07-29 | 2018-02-01 | Johnson Controls Technology Company | Electrical energy storage system with battery state-of-charge estimation |
| CN112531792A (en) * | 2020-12-03 | 2021-03-19 | 江苏方天电力技术有限公司 | Frequency control method and system for interconnected power system containing energy storage resources |
| CN112542845A (en) * | 2020-11-30 | 2021-03-23 | 国网浙江省电力有限公司电力科学研究院 | Energy storage power station participating peak regulation control method considering frequency support capability |
| CN112564135A (en) * | 2020-12-22 | 2021-03-26 | 浙江大学 | New energy and energy storage power station group frequency/voltage coordination control method and device |
| CN114336673A (en) * | 2021-11-24 | 2022-04-12 | 华能新能源股份有限公司 | Wind storage combined power station primary frequency modulation control strategy based on model predictive control |
| CN115473243A (en) * | 2022-09-20 | 2022-12-13 | 国网甘肃省电力公司电力科学研究院 | Energy storage configuration analysis method and device for regional power grid frequency modulation and voltage regulation requirements, electronic equipment and medium |
-
2023
- 2023-03-09 CN CN202310221747.9A patent/CN115882480B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180034285A1 (en) * | 2016-07-29 | 2018-02-01 | Johnson Controls Technology Company | Electrical energy storage system with battery state-of-charge estimation |
| CN112542845A (en) * | 2020-11-30 | 2021-03-23 | 国网浙江省电力有限公司电力科学研究院 | Energy storage power station participating peak regulation control method considering frequency support capability |
| CN112531792A (en) * | 2020-12-03 | 2021-03-19 | 江苏方天电力技术有限公司 | Frequency control method and system for interconnected power system containing energy storage resources |
| CN112564135A (en) * | 2020-12-22 | 2021-03-26 | 浙江大学 | New energy and energy storage power station group frequency/voltage coordination control method and device |
| CN114336673A (en) * | 2021-11-24 | 2022-04-12 | 华能新能源股份有限公司 | Wind storage combined power station primary frequency modulation control strategy based on model predictive control |
| CN115473243A (en) * | 2022-09-20 | 2022-12-13 | 国网甘肃省电力公司电力科学研究院 | Energy storage configuration analysis method and device for regional power grid frequency modulation and voltage regulation requirements, electronic equipment and medium |
Non-Patent Citations (5)
| Title |
|---|
| WEI HE: "Optimal_Scheduling_of_Combined_Heat_and_Power_Based_Microgrid", 《2020 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA TECHNICAL CONFERENCE》 * |
| WEI ZENG: "Distributed_Photovoltaic_Voltage_Coordinated_Control_Strategy_Based_on_Deep_Learning", 《2021 IEEE SUSTAINABLE POWER AND ENERGY CONFERENCE (ISPEC)》 * |
| 曾伟: ""双碳"目标下智慧社区新能源消纳的政策与技术研究", 《价格理论与实践》 * |
| 谢 秋 实: "电池储能参与含风电力系统调峰与优化调度", 《中国优秀硕士学位论文全文数据库》 * |
| 陈旭: "分布式电源接入配电网的规划与电压控制方法研究", 《中国博士学位论文全文数据库》 * |
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